Corspedia | Machine Learning with R

Overview

This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.

Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.

Get ready to do more learning than your machine!

Syllabus

Module 1 - Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

Machine Learning Languages, Types, and Examples

Machine Learning vs Statistical Modelling

Supervised vs Unsupervised Learning

Supervised Learning Classification

Unsupervised Learning

Module 2 - Supervised Learning I

K-Nearest Neighbors

Decision Trees

Random Forests

Reliability of Random Forests

Advantages & Disadvantages of Decision Trees

Module 3 - Supervised Learning II

Regression Algorithms

Model Evaluation

Model Evaluation: Overfitting & Underfitting

Understanding Different Evaluation Models

Module 4 - Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages

Hierarchical Clustering plus Advantages & Disadvantages

Measuring the Distances Between Clusters - Single Linkage Clustering

Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering

Density-Based Clustering

Module 5 - Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection

Collaborative Filtering & Its Challenges

Machine Learning with R

Brought by: Cognitive Class

Overview

Syllabus

Machine Learning with R

Brought by: Cognitive Class